Generate RAG project scaffolds for LangChain, LlamaIndex, and vector stores.
npx skills add https://github.com/floflo777/claude-rag-skills --skill rag-scaffold
Please help me install this Agent Skill. Skill: rag-scaffold Source: https://github.com/floflo777/claude-rag-skills/tree/main/rag-scaffold Command: npx skills add https://github.com/floflo777/claude-rag-skills --skill rag-scaffold
This Skill provides ready-to-use templates and scaffolding to bootstrap production-grade RAG pipelines, enabling teams to start coding faster and avoid boilerplate drift.
After installation, invoke /rag-scaffold in your Claude Code session. The skill will prompt you for your framework, vector store, and LLM provider, then generate a ready-to-run scaffold you can immediately customize.
High-intent search queries and answers about installing and using this skill.
You can generate a production-ready RAG pipeline boilerplate by using a scaffolding tool that prompts for your framework, vector store, and LLM provider, then outputs a customizable project structure with indexing, config, and orchestration.
Yes, you can scaffold a RAG project using LangChain and Qdrant together. The skill provides framework templates for LangChain and vector store quick-starts for Qdrant, generating an integrated, ready-to-run pipeline configuration.
This RAG scaffolding tool supports TypeScript through LangChain.js templates and Python through LlamaIndex templates, allowing you to bootstrap projects across multiple common tech stacks and deployment environments.
The best way to bootstrap a documentation search RAG service is to use a project scaffolding generator that outputs best-practice project structures with configurable components for vector storage and pipeline orchestration.
The generated RAG project relies on dependencies including openai, qdrant-client, langchain, tiktoken, and pydantic-settings to manage configurations, embeddings, tokenization, and vector storage integration within the pipeline.
When bootstrapping a RAG project, available vector store templates include Qdrant, Pinecone, ChromaDB, Weaviate, and Milvus, allowing you to adapt the generated scaffold to your specific storage choice.
ShenSeanChenBootstrap a FastAPI backend for RAG with repository cloning and environment setup.
chicagopeabodydev-sudoIngest and index domain-specific data, then query it with LlamaIndex for retrieval-augmented generation.
patricio0312revAutomate document embedding pipelines for semantic search.
floflo777Generate RAG project scaffolds for LangChain, LlamaIndex, and vector stores.
